본문 바로가기
[학술저널]

  • 학술저널

Kyeong-Seop Kim(Konkuk University)

UCI(KEPA) : I410-ECN-0101-2018-004-000875677

초록

Premature Ventricular Contraction(PVC) arrhythmia is most common abnormal-heart rhythm that may increase mortal risk of a cardiac patient. Thus, it is very important issue to identify the specular portraits of PVC pattern especially from the patient. In this paper, we propose a new method to extract the characteristics of PVC pattern by applying K-means machine learning algorithm on Heart Rate Variability depicted in Poinecare plot. For the quantitative analysis to distinguish the trend of cluster patterns between normal sinus rhythm and PVC beat, the Euclidean distance measure was sought between the clusters. Experimental simulations on MIT-BIH arrhythmia database draw the fact that the distance measure on the cluster is valid for differentiating the pattern-traits of PVC beats. Therefore, we proposed a method that can offer the simple remedy to identify the attributes of PVC beats in terms of K-means clusters especially in the long-period Electrocardiogram(ECG).

목차

Abstract
I. Introduction
II. Preliminaries
III. The Proposed Scheme
IV. Experiment
V. Conclusions
REFERENCES

리뷰(0)

도움이 되었어요.0

도움이 안되었어요.0

첫 리뷰를 남겨주세요.
DBpia에서 서비스 중인 논문에 한하여 피인용 수가 반영됩니다.
인용된 논문이 DBpia에서 서비스 중이라면, 아래 [참고문헌 신청]을 통해서 등록해보세요.
Insert title here